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未验证 提交 c49f35cf 编写于 作者: Z zhangchunle 提交者： GitHub
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[part1] fix sign-compare warning (#43276) 

* fix sign-compare warning

* fix sign-compare 2
上级 caa57498
隐藏空白更改
内联 并排
Showing with 114 addition and 113 deletion
paddle/fluid/distributed/ps/service/brpc_ps_client.cc
浏览文件 @ c49f35cf
......@@ -197,7 +197,7 @@ int32_t BrpcPsClient::Initialize() {
// 异步push 请求队列初始化
const auto &worker_param = _config.worker_param().downpour_worker_param();
for (size_t i = 0; i < worker_param.downpour_table_param_size(); ++i) {
for (int i = 0; i < worker_param.downpour_table_param_size(); ++i) {
auto type = worker_param.downpour_table_param(i).type();
auto table_id = worker_param.downpour_table_param(i).table_id();
if (type == PS_DENSE_TABLE) {
......@@ -662,7 +662,7 @@ std::future<int32_t> BrpcPsClient::PushSparseParam(size_t table_id,
char *push_data_ptr = const_cast<char *>(push_data->data());
memcpy(push_data_ptr, kvs.data(), kv_size * sizeof(uint64_t));
push_data_ptr += kv_size * sizeof(uint64_t);
for (int i = 0; i < kv_size; ++i) {
for (size_t i = 0; i < kv_size; ++i) {
memcpy(push_data_ptr, value_ptr[i], value_size);
push_data_ptr += value_size;
}
......@@ -882,7 +882,7 @@ std::future<int32_t> BrpcPsClient::PushSparseRawGradient(
memcpy(push_data_ptr, kvs.data(), kv_size * sizeof(uint64_t));
push_data_ptr += kv_size * sizeof(uint64_t);
for (int i = 0; i < kv_size; ++i) {
for (size_t i = 0; i < kv_size; ++i) {
memcpy(push_data_ptr, value_ptr[i], value_size);
push_data_ptr += value_size;
}
......@@ -1237,7 +1237,7 @@ std::future<int32_t> BrpcPsClient::PushSparseRawGradientPartial(
char *push_data_ptr = const_cast<char *>(push_data->data());
memcpy(push_data_ptr, keys, num * sizeof(uint64_t));
push_data_ptr += num * sizeof(uint64_t);
for (int i = 0; i < num; ++i) {
for (uint32_t i = 0; i < num; ++i) {
memcpy(push_data_ptr, update_values[i], value_size);
push_data_ptr += value_size;
}
......@@ -1257,7 +1257,7 @@ int32_t BrpcPsClient::RecvAndSaveTable(const uint64_t table_id,
int64_t var_shape = 0;
std::string table_class;
const auto &worker_param = _config.worker_param().downpour_worker_param();
for (size_t i = 0; i < worker_param.downpour_table_param_size(); ++i) {
for (int i = 0; i < worker_param.downpour_table_param_size(); ++i) {
if (worker_param.downpour_table_param(i).table_id() == table_id) {
var_name = worker_param.downpour_table_param(i).common().table_name();
var_num = worker_param.downpour_table_param(i).common().table_num();
......@@ -1481,13 +1481,13 @@ void BrpcPsClient::PushSparseTaskConsume() {
closure->add_timer(rpc_timer);
std::vector<std::future<int>> merge_status(request_call_num);
for (int shard_idx = 0; shard_idx < request_call_num; ++shard_idx) {
for (size_t shard_idx = 0; shard_idx < request_call_num; ++shard_idx) {
merge_status[shard_idx] =
async_push_sparse_shard_threads.enqueue(std::bind(
&BrpcPsClient::PushSparseAsyncShardPush, this, task_list,
request_kv_num, table_id, shard_idx, closure, accessor));
}
for (int shard_idx = 0; shard_idx < request_call_num; ++shard_idx) {
for (size_t shard_idx = 0; shard_idx < request_call_num; ++shard_idx) {
merge_status[shard_idx].wait();
}
merge_status.clear();
......@@ -1497,13 +1497,13 @@ void BrpcPsClient::PushSparseTaskConsume() {
auto queue_size = task_queue->Size();
} else { // 未达到阈值 只做多路归并
std::vector<std::future<int>> merge_status(request_call_num);
for (int shard_idx = 0; shard_idx < request_call_num; ++shard_idx) {
for (size_t shard_idx = 0; shard_idx < request_call_num; ++shard_idx) {
merge_status[shard_idx] =
async_push_sparse_shard_threads.enqueue(std::bind(
&BrpcPsClient::PushSparseAsyncShardMerge, this, task_list,
request_kv_num, table_id, shard_idx, accessor));
}
for (int shard_idx = 0; shard_idx < request_call_num; ++shard_idx) {
for (size_t shard_idx = 0; shard_idx < request_call_num; ++shard_idx) {
merge_status[shard_idx].wait();
}
......@@ -1529,7 +1529,7 @@ void sparse_local_merge(ValueAccessor *accessor, float *merge_data,
size_t col_num = accessor->GetAccessorInfo().update_dim;
float *merge_data_shell[col_num];
const float *another_data_shell[col_num];
for (int i = 0; i < col_num; ++i) {
for (size_t i = 0; i < col_num; ++i) {
merge_data_shell[i] = merge_data + i;
another_data_shell[i] = another_data + i;
}
......@@ -1546,12 +1546,12 @@ int BrpcPsClient::PushSparseAsyncShardMerge(
thread_local std::vector<std::pair<uint64_t, const float *>> sorted_kv_list;
sorted_kv_list.clear();
for (int i = 1; i < task_list.size(); ++i) {
for (size_t i = 1; i < task_list.size(); ++i) {
size_t kv_num = task_list[i]->data()->shared_data[shard_idx].kv_num;
auto &key_list = task_list[i]->data()->shared_data[shard_idx].key_list;
auto &value_list = task_list[i]->data()->shared_data[shard_idx].value_list;
for (int j = 0; j < kv_num; ++j) {
for (size_t j = 0; j < kv_num; ++j) {
if (value_list[j].size() < value_size) {
LOG(WARNING) << "value_list[" << j << "]: " << value_list[j].c_str()
<< "is invalid.";
......@@ -1654,7 +1654,7 @@ int BrpcPsClient::PushSparseAsyncShardPush(
memcpy(push_data_ptr, merged_key_list.data(),
merged_kv_count * sizeof(uint64_t));
push_data_ptr += merged_kv_count * sizeof(uint64_t);
for (int i = 0; i < merged_kv_count; ++i) {
for (size_t i = 0; i < merged_kv_count; ++i) {
const char *task_data_ptr = merged_value_list[i].data();
memcpy(push_data_ptr, (float *)(task_data_ptr), // NOLINT
......@@ -1778,7 +1778,7 @@ void BrpcPsClient::PushDenseTaskConsume() {
});
++merge_count;
}
for (int i = 0; i < merge_count; ++i) {
for (uint32_t i = 0; i < merge_count; ++i) {
merge_status[i].wait();
}
......
paddle/fluid/distributed/ps/service/graph_brpc_client.cc
浏览文件 @ c49f35cf
......@@ -60,7 +60,7 @@ std::future<int32_t> GraphBrpcClient::get_node_feat(
std::vector<std::vector<std::string>> &res) {
std::vector<int> request2server;
std::vector<int> server2request(server_size, -1);
for (int query_idx = 0; query_idx < node_ids.size(); ++query_idx) {
for (size_t query_idx = 0; query_idx < node_ids.size(); ++query_idx) {
int server_index = get_server_index_by_id(node_ids[query_idx]);
if (server2request[server_index] == -1) {
server2request[server_index] = request2server.size();
......@@ -70,7 +70,7 @@ std::future<int32_t> GraphBrpcClient::get_node_feat(
size_t request_call_num = request2server.size();
std::vector<std::vector<int64_t>> node_id_buckets(request_call_num);
std::vector<std::vector<int>> query_idx_buckets(request_call_num);
for (int query_idx = 0; query_idx < node_ids.size(); ++query_idx) {
for (size_t query_idx = 0; query_idx < node_ids.size(); ++query_idx) {
int server_index = get_server_index_by_id(node_ids[query_idx]);
int request_idx = server2request[server_index];
node_id_buckets[request_idx].push_back(node_ids[query_idx]);
......@@ -83,7 +83,7 @@ std::future<int32_t> GraphBrpcClient::get_node_feat(
int ret = 0;
auto *closure = (DownpourBrpcClosure *)done;
size_t fail_num = 0;
for (int request_idx = 0; request_idx < request_call_num;
for (size_t request_idx = 0; request_idx < request_call_num;
++request_idx) {
if (closure->check_response(request_idx, PS_GRAPH_GET_NODE_FEAT) !=
0) {
......@@ -122,7 +122,7 @@ std::future<int32_t> GraphBrpcClient::get_node_feat(
closure->add_promise(promise);
std::future<int> fut = promise->get_future();
for (int request_idx = 0; request_idx < request_call_num; ++request_idx) {
for (size_t request_idx = 0; request_idx < request_call_num; ++request_idx) {
int server_index = request2server[request_idx];
closure->request(request_idx)->set_cmd_id(PS_GRAPH_GET_NODE_FEAT);
closure->request(request_idx)->set_table_id(table_id);
......@@ -271,7 +271,7 @@ std::future<int32_t> GraphBrpcClient::remove_graph_node(
request_call_num, [&, request_call_num](void *done) {
int ret = 0;
auto *closure = (DownpourBrpcClosure *)done;
int fail_num = 0;
size_t fail_num = 0;
for (size_t request_idx = 0; request_idx < request_call_num;
++request_idx) {
if (closure->check_response(request_idx,
......@@ -378,7 +378,7 @@ std::future<int32_t> GraphBrpcClient::batch_sample_neighbors(
std::vector<int> server2request(server_size, -1);
res.clear();
res_weight.clear();
for (int query_idx = 0; query_idx < node_ids.size(); ++query_idx) {
for (size_t query_idx = 0; query_idx < node_ids.size(); ++query_idx) {
int server_index = get_server_index_by_id(node_ids[query_idx]);
if (server2request[server_index] == -1) {
server2request[server_index] = request2server.size();
......@@ -393,7 +393,7 @@ std::future<int32_t> GraphBrpcClient::batch_sample_neighbors(
size_t request_call_num = request2server.size();
std::vector<std::vector<int64_t>> node_id_buckets(request_call_num);
std::vector<std::vector<int>> query_idx_buckets(request_call_num);
for (int query_idx = 0; query_idx < node_ids.size(); ++query_idx) {
for (size_t query_idx = 0; query_idx < node_ids.size(); ++query_idx) {
int server_index = get_server_index_by_id(node_ids[query_idx]);
int request_idx = server2request[server_index];
node_id_buckets[request_idx].push_back(node_ids[query_idx]);
......@@ -454,7 +454,7 @@ std::future<int32_t> GraphBrpcClient::batch_sample_neighbors(
closure->add_promise(promise);
std::future<int> fut = promise->get_future();
for (int request_idx = 0; request_idx < request_call_num; ++request_idx) {
for (size_t request_idx = 0; request_idx < request_call_num; ++request_idx) {
int server_index = request2server[request_idx];
closure->request(request_idx)->set_cmd_id(PS_GRAPH_SAMPLE_NEIGHBORS);
closure->request(request_idx)->set_table_id(table_id);
......@@ -492,7 +492,7 @@ std::future<int32_t> GraphBrpcClient::random_sample_nodes(
size_t bytes_size = io_buffer_itr.bytes_left();
char *buffer = new char[bytes_size];
auto size = io_buffer_itr.copy_and_forward((void *)(buffer), bytes_size);
int index = 0;
size_t index = 0;
while (index < bytes_size) {
ids.push_back(*(int64_t *)(buffer + index));
index += GraphNode::id_size;
......@@ -534,7 +534,7 @@ std::future<int32_t> GraphBrpcClient::pull_graph_list(
size_t bytes_size = io_buffer_itr.bytes_left();
char *buffer = new char[bytes_size];
io_buffer_itr.copy_and_forward((void *)(buffer), bytes_size);
int index = 0;
size_t index = 0;
while (index < bytes_size) {
FeatureNode node;
node.recover_from_buffer(buffer + index);
......@@ -570,7 +570,7 @@ std::future<int32_t> GraphBrpcClient::set_node_feat(
const std::vector<std::vector<std::string>> &features) {
std::vector<int> request2server;
std::vector<int> server2request(server_size, -1);
for (int query_idx = 0; query_idx < node_ids.size(); ++query_idx) {
for (size_t query_idx = 0; query_idx < node_ids.size(); ++query_idx) {
int server_index = get_server_index_by_id(node_ids[query_idx]);
if (server2request[server_index] == -1) {
server2request[server_index] = request2server.size();
......@@ -582,7 +582,7 @@ std::future<int32_t> GraphBrpcClient::set_node_feat(
std::vector<std::vector<int>> query_idx_buckets(request_call_num);
std::vector<std::vector<std::vector<std::string>>> features_idx_buckets(
request_call_num);
for (int query_idx = 0; query_idx < node_ids.size(); ++query_idx) {
for (size_t query_idx = 0; query_idx < node_ids.size(); ++query_idx) {
int server_index = get_server_index_by_id(node_ids[query_idx]);
int request_idx = server2request[server_index];
node_id_buckets[request_idx].push_back(node_ids[query_idx]);
......@@ -590,7 +590,7 @@ std::future<int32_t> GraphBrpcClient::set_node_feat(
if (features_idx_buckets[request_idx].size() == 0) {
features_idx_buckets[request_idx].resize(feature_names.size());
}
for (int feat_idx = 0; feat_idx < feature_names.size(); ++feat_idx) {
for (size_t feat_idx = 0; feat_idx < feature_names.size(); ++feat_idx) {
features_idx_buckets[request_idx][feat_idx].push_back(
features[feat_idx][query_idx]);
}
......@@ -602,7 +602,7 @@ std::future<int32_t> GraphBrpcClient::set_node_feat(
int ret = 0;
auto *closure = (DownpourBrpcClosure *)done;
size_t fail_num = 0;
for (int request_idx = 0; request_idx < request_call_num;
for (size_t request_idx = 0; request_idx < request_call_num;
++request_idx) {
if (closure->check_response(request_idx, PS_GRAPH_SET_NODE_FEAT) !=
0) {
......@@ -619,7 +619,7 @@ std::future<int32_t> GraphBrpcClient::set_node_feat(
closure->add_promise(promise);
std::future<int> fut = promise->get_future();
for (int request_idx = 0; request_idx < request_call_num; ++request_idx) {
for (size_t request_idx = 0; request_idx < request_call_num; ++request_idx) {
int server_index = request2server[request_idx];
closure->request(request_idx)->set_cmd_id(PS_GRAPH_SET_NODE_FEAT);
closure->request(request_idx)->set_table_id(table_id);
......
paddle/fluid/distributed/ps/service/graph_brpc_server.cc
浏览文件 @ c49f35cf
......@@ -516,7 +516,7 @@ int32_t GraphBrpcService::sample_neighbors_across_multi_servers(
std::vector<int64_t> local_id;
std::vector<int> local_query_idx;
size_t rank = GetRank();
for (int query_idx = 0; query_idx < node_num; ++query_idx) {
for (size_t query_idx = 0; query_idx < node_num; ++query_idx) {
int server_index =
((GraphTable *)table)->get_server_index_by_id(node_data[query_idx]);
if (server2request[server_index] == -1) {
......@@ -538,7 +538,7 @@ int32_t GraphBrpcService::sample_neighbors_across_multi_servers(
std::vector<size_t> seq;
std::vector<std::vector<int64_t>> node_id_buckets(request_call_num);
std::vector<std::vector<int>> query_idx_buckets(request_call_num);
for (int query_idx = 0; query_idx < node_num; ++query_idx) {
for (size_t query_idx = 0; query_idx < node_num; ++query_idx) {
int server_index =
((GraphTable *)table)->get_server_index_by_id(node_data[query_idx]);
int request_idx = server2request[server_index];
......@@ -614,7 +614,7 @@ int32_t GraphBrpcService::sample_neighbors_across_multi_servers(
closure->add_promise(promise);
std::future<int> fut = promise->get_future();
for (int request_idx = 0; request_idx < remote_call_num; ++request_idx) {
for (size_t request_idx = 0; request_idx < remote_call_num; ++request_idx) {
int server_index = request2server[request_idx];
closure->request(request_idx)->set_cmd_id(PS_GRAPH_SAMPLE_NEIGHBORS);
closure->request(request_idx)->set_table_id(request.table_id());
......
paddle/fluid/distributed/ps/table/ctr_accessor.cc
浏览文件 @ c49f35cf
......@@ -196,7 +196,7 @@ bool CtrCommonAccessor::NeedExtendMF(float* value) {
return score >= _config.embedx_threshold();
}
bool CtrCommonAccessor::HasMF(size_t size) {
bool CtrCommonAccessor::HasMF(int size) {
return size > common_feature_value.EmbedxG2SumIndex();
}
......@@ -227,11 +227,11 @@ int32_t CtrCommonAccessor::Merge(float** update_values,
const float** other_update_values,
size_t num) {
auto embedx_dim = _config.embedx_dim();
size_t total_dim = CtrCommonPushValue::Dim(embedx_dim);
int total_dim = CtrCommonPushValue::Dim(embedx_dim);
for (size_t value_item = 0; value_item < num; ++value_item) {
float* update_value = update_values[value_item];
const float* other_update_value = other_update_values[value_item];
for (auto i = 0u; i < total_dim; ++i) {
for (int i = 0; i < total_dim; ++i) {
if (i != CtrCommonPushValue::SlotIndex()) {
update_value[i] += other_update_value[i];
}
......
paddle/fluid/distributed/ps/table/ctr_accessor.h
浏览文件 @ c49f35cf
......@@ -143,7 +143,7 @@ class CtrCommonAccessor : public ValueAccessor {
// 判断该value是否保存到ssd
// virtual bool save_ssd(float* value);
virtual bool NeedExtendMF(float* value);
virtual bool HasMF(size_t size);
virtual bool HasMF(int size);
// 判断该value是否在save阶段dump,
// param作为参数用于标识save阶段，如downpour的xbox与batch_model
// param = 0, save all feature
......
paddle/fluid/distributed/ps/table/ctr_double_accessor.cc
浏览文件 @ c49f35cf
......@@ -139,7 +139,7 @@ bool CtrDoubleAccessor::Save(float* value, int param) {
}
default:
return true;
};
}
}
void CtrDoubleAccessor::UpdateStatAfterSave(float* value, int param) {
......@@ -166,7 +166,7 @@ void CtrDoubleAccessor::UpdateStatAfterSave(float* value, int param) {
return;
default:
return;
};
}
}
int32_t CtrDoubleAccessor::Create(float** values, size_t num) {
......@@ -175,7 +175,7 @@ int32_t CtrDoubleAccessor::Create(float** values, size_t num) {
float* value = values[value_item];
value[CtrDoubleFeatureValue::UnseenDaysIndex()] = 0;
value[CtrDoubleFeatureValue::DeltaScoreIndex()] = 0;
*(double*)(value + CtrDoubleFeatureValue::ShowIndex()) = 0;
*reinterpret_cast<double*>(value + CtrDoubleFeatureValue::ShowIndex()) = 0;
*(double*)(value + CtrDoubleFeatureValue::ClickIndex()) = 0;
value[CtrDoubleFeatureValue::SlotIndex()] = -1;
_embed_sgd_rule->InitValue(
......@@ -233,7 +233,7 @@ int32_t CtrDoubleAccessor::Merge(float** update_values,
for (auto i = 3u; i < total_dim; ++i) {
update_value[i] += other_update_value[i];
}*/
for (auto i = 0u; i < total_dim; ++i) {
for (size_t i = 0; i < total_dim; ++i) {
if (i != CtrDoublePushValue::SlotIndex()) {
update_value[i] += other_update_value[i];
}
......@@ -320,7 +320,7 @@ std::string CtrDoubleAccessor::ParseToString(const float* v, int param_size) {
auto score = ShowClickScore(show, click);
if (score >= _config.embedx_threshold() && param_size > 9) {
os << " " << v[9];
for (auto i = 0; i < _config.embedx_dim(); ++i) {
for (size_t i = 0; i < _config.embedx_dim(); ++i) {
os << " " << v[10 + i];
}
}
......
paddle/fluid/distributed/ps/table/ctr_dymf_accessor.cc
浏览文件 @ c49f35cf
......@@ -198,7 +198,7 @@ bool CtrDymfAccessor::NeedExtendMF(float* value) {
return score >= _config.embedx_threshold();
}
bool CtrDymfAccessor::HasMF(size_t size) {
bool CtrDymfAccessor::HasMF(int size) {
return size > common_feature_value.EmbedxG2SumIndex();
}
......
paddle/fluid/distributed/ps/table/ctr_dymf_accessor.h
浏览文件 @ c49f35cf
......@@ -158,7 +158,7 @@ class CtrDymfAccessor : public ValueAccessor {
// 判断该value是否保存到ssd
// virtual bool save_ssd(float* value);
virtual bool NeedExtendMF(float* value);
virtual bool HasMF(size_t size);
virtual bool HasMF(int size);
// 判断该value是否在save阶段dump,
// param作为参数用于标识save阶段，如downpour的xbox与batch_model
// param = 0, save all feature
......
paddle/fluid/distributed/ps/table/memory_dense_table.cc
浏览文件 @ c49f35cf
......@@ -41,7 +41,7 @@ void MemoryDenseTable::CreateInitializer(const std::string& attr,
int32_t MemoryDenseTable::Initialize() {
_shards_task_pool.resize(task_pool_size_);
for (int i = 0; i < _shards_task_pool.size(); ++i) {
for (size_t i = 0; i < _shards_task_pool.size(); ++i) {
_shards_task_pool[i].reset(new ::ThreadPool(1));
}
......@@ -74,14 +74,14 @@ int32_t MemoryDenseTable::InitializeValue() {
values_[x].resize(dim);
names_index_[varname] = x;
for (int y = 0; y < dim; ++y) {
for (size_t y = 0; y < dim; ++y) {
values_[x][y] = initializers_[varname]->GetValue();
}
}
fixed_len_params_dim_ = 0;
for (int x = 0; x < size; ++x) {
auto& dim = common.dims()[x];
int dim = common.dims()[x];
if (dim != param_dim_) {
fixed_len_params_dim_ += dim;
} else {
......@@ -245,14 +245,14 @@ int32_t MemoryDenseTable::Load(const std::string& path,
do {
is_read_failed = false;
try {
size_t dim_idx = 0;
int dim_idx = 0;
float data_buffer[5];
float* data_buff_ptr = data_buffer;
std::string line_data;
int size = static_cast<int>(values_.size());
auto common = _config.common();
for (int i = start_file_idx; i < end_file_idx + 1; ++i) {
for (size_t i = start_file_idx; i < end_file_idx + 1; ++i) {
channel_config.path = file_list[i];
err_no = 0;
auto read_channel = _afs_client.open_r(channel_config, 0, &err_no);
......@@ -271,12 +271,12 @@ int32_t MemoryDenseTable::Load(const std::string& path,
if (file_dim_idx < file_start_idx) {
continue;
}
auto str_len =
size_t str_len =
paddle::string::str_to_float(line_data.data(), data_buff_ptr);
CHECK(str_len == param_col_ids_.size())
<< "expect " << param_col_ids_.size() << " float, but got "
<< str_len;
for (size_t col_idx = 0; col_idx < str_len; ++col_idx) {
for (int col_idx = 0; col_idx < str_len; ++col_idx) {
if (param_col_ids_[col_idx] < 0) {
continue;
}
......@@ -355,7 +355,7 @@ int32_t MemoryDenseTable::Save(const std::string& path,
std::ostringstream os;
for (int x = 0; x < size; ++x) {
auto& varname = common.params()[x];
auto& dim = common.dims()[x];
int dim = common.dims()[x];
VLOG(3) << "MemoryDenseTable::save dim " << x << " size: " << dim;
for (int y = 0; y < dim; ++y) {
os.clear();
......
paddle/fluid/distributed/ps/table/memory_sparse_geo_table.cc
浏览文件 @ c49f35cf
......@@ -49,7 +49,7 @@ int32_t MemorySparseGeoTable::PushSparseParam(const uint64_t* keys,
std::vector<std::vector<uint64_t>> offset_bucket;
offset_bucket.resize(shard_num);
for (int x = 0; x < num; ++x) {
for (size_t x = 0; x < num; ++x) {
auto y = keys[x] % shard_num;
offset_bucket[y].push_back(x);
if (x < 10) {
......@@ -66,7 +66,7 @@ int32_t MemorySparseGeoTable::PushSparseParam(const uint64_t* keys,
auto& local_shard = _local_shards[shard_id];
auto& offsets = offset_bucket[shard_id];
for (int i = 0; i < offsets.size(); ++i) {
for (size_t i = 0; i < offsets.size(); ++i) {
auto offset = offsets[i];
auto id = keys[offset];
auto& feature_value = local_shard[id];
......@@ -132,7 +132,7 @@ int32_t MemorySparseGeoTable::Initialize() {
_dim = _config.common().dims()[0];
_shards_task_pool.resize(_task_pool_size);
for (int i = 0; i < _shards_task_pool.size(); ++i) {
for (size_t i = 0; i < _shards_task_pool.size(); ++i) {
_shards_task_pool[i].reset(new ::ThreadPool(1));
}
......@@ -200,14 +200,14 @@ int32_t MemorySparseGeoTable::_PushSparse(const uint64_t* keys,
task_keys[shard_id].push_back({keys[i], i});
}
for (size_t shard_id = 0; shard_id < shard_num; ++shard_id) {
for (int shard_id = 0; shard_id < shard_num; ++shard_id) {
tasks[shard_id] = _shards_task_pool[shard_id]->enqueue(
[this, shard_id, values, &task_keys]() -> int {
auto& keys = task_keys[shard_id];
auto& local_shard = _local_shards[shard_id];
auto blas = GetBlas<float>();
for (int i = 0; i < keys.size(); ++i) {
for (size_t i = 0; i < keys.size(); ++i) {
uint64_t key = keys[i].first;
uint64_t push_data_idx = keys[i].second;
const float* update_data = values + push_data_idx * _dim;
......
paddle/fluid/distributed/ps/table/memory_sparse_table.cc
浏览文件 @ c49f35cf
......@@ -37,7 +37,7 @@ namespace distributed {
int32_t MemorySparseTable::Initialize() {
_shards_task_pool.resize(_task_pool_size);
for (int i = 0; i < _shards_task_pool.size(); ++i) {
for (size_t i = 0; i < _shards_task_pool.size(); ++i) {
_shards_task_pool[i].reset(new ::ThreadPool(1));
}
auto& profiler = CostProfiler::instance();
......@@ -79,7 +79,7 @@ int32_t MemorySparseTable::Load(const std::string& path,
}
int load_param = atoi(param.c_str());
auto expect_shard_num = _sparse_table_shard_num;
size_t expect_shard_num = _sparse_table_shard_num;
if (file_list.size() != expect_shard_num) {
LOG(WARNING) << "MemorySparseTable file_size:" << file_list.size()
<< " not equal to expect_shard_num:" << expect_shard_num;
......@@ -98,7 +98,7 @@ int32_t MemorySparseTable::Load(const std::string& path,
int thread_num = _real_local_shard_num < 15 ? _real_local_shard_num : 15;
omp_set_num_threads(thread_num);
#pragma omp parallel for schedule(dynamic)
for (size_t i = 0; i < _real_local_shard_num; ++i) {
for (int i = 0; i < _real_local_shard_num; ++i) {
FsChannelConfig channel_config;
channel_config.path = file_list[file_start_idx + i];
VLOG(1) << "MemorySparseTable::load begin load " << channel_config.path
......@@ -164,7 +164,7 @@ int32_t MemorySparseTable::LoadLocalFS(const std::string& path,
auto file_list = paddle::framework::localfs_list(table_path);
int load_param = atoi(param.c_str());
auto expect_shard_num = _sparse_table_shard_num;
size_t expect_shard_num = _sparse_table_shard_num;
if (file_list.size() != expect_shard_num) {
LOG(WARNING) << "MemorySparseTable file_size:" << file_list.size()
<< " not equal to expect_shard_num:" << expect_shard_num;
......@@ -183,7 +183,7 @@ int32_t MemorySparseTable::LoadLocalFS(const std::string& path,
int thread_num = _real_local_shard_num < 15 ? _real_local_shard_num : 15;
omp_set_num_threads(thread_num);
#pragma omp parallel for schedule(dynamic)
for (size_t i = 0; i < _real_local_shard_num; ++i) {
for (int i = 0; i < _real_local_shard_num; ++i) {
bool is_read_failed = false;
int retry_num = 0;
int err_no = 0;
......@@ -244,7 +244,7 @@ int32_t MemorySparseTable::Save(const std::string& dirname,
int thread_num = _real_local_shard_num < 20 ? _real_local_shard_num : 20;
omp_set_num_threads(thread_num);
#pragma omp parallel for schedule(dynamic)
for (size_t i = 0; i < _real_local_shard_num; ++i) {
for (int i = 0; i < _real_local_shard_num; ++i) {
FsChannelConfig channel_config;
if (_config.compress_in_save() && (save_param == 0 || save_param == 3)) {
channel_config.path = paddle::string::format_string(
......@@ -326,7 +326,7 @@ int32_t MemorySparseTable::SaveLocalFS(const std::string& dirname,
omp_set_num_threads(thread_num);
#pragma omp parallel for schedule(dynamic)
for (size_t i = 0; i < _real_local_shard_num; ++i) {
for (int i = 0; i < _real_local_shard_num; ++i) {
feasign_cnt = 0;
auto& shard = _local_shards[i];
std::string file_name = paddle::string::format_string(
......@@ -354,7 +354,7 @@ int32_t MemorySparseTable::SaveLocalFS(const std::string& dirname,
int64_t MemorySparseTable::LocalSize() {
int64_t local_size = 0;
for (size_t i = 0; i < _real_local_shard_num; ++i) {
for (int i = 0; i < _real_local_shard_num; ++i) {
local_size += _local_shards[i].size();
}
return local_size;
......@@ -364,7 +364,7 @@ int64_t MemorySparseTable::LocalMFSize() {
std::vector<int64_t> size_arr(_real_local_shard_num, 0);
std::vector<std::future<int>> tasks(_real_local_shard_num);
int64_t ret_size = 0;
for (size_t shard_id = 0; shard_id < _real_local_shard_num; ++shard_id) {
for (int shard_id = 0; shard_id < _real_local_shard_num; ++shard_id) {
tasks[shard_id] =
_shards_task_pool[shard_id % _shards_task_pool.size()]->enqueue(
[this, shard_id, &size_arr]() -> int {
......@@ -378,7 +378,7 @@ int64_t MemorySparseTable::LocalMFSize() {
return 0;
});
}
for (size_t i = 0; i < _real_local_shard_num; ++i) {
for (int i = 0; i < _real_local_shard_num; ++i) {
tasks[i].wait();
}
for (auto x : size_arr) {
......@@ -469,7 +469,7 @@ int32_t MemorySparseTable::PullSparse(float* pull_values,
memcpy(data_buffer_ptr, itr.value().data(),
data_size * sizeof(float));
}
for (int mf_idx = data_size; mf_idx < value_size; ++mf_idx) {
for (size_t mf_idx = data_size; mf_idx < value_size; ++mf_idx) {
data_buffer[mf_idx] = 0.0;
}
auto offset = keys[i].second;
......@@ -503,7 +503,7 @@ int32_t MemorySparseTable::PullSparsePtr(char** pull_values,
task_keys[shard_id].push_back({keys[i], i});
}
// std::atomic<uint32_t> missed_keys{0};
for (size_t shard_id = 0; shard_id < _real_local_shard_num; ++shard_id) {
for (int shard_id = 0; shard_id < _real_local_shard_num; ++shard_id) {
tasks[shard_id] =
_shards_task_pool[shard_id % _shards_task_pool.size()]->enqueue(
[this, shard_id, &task_keys, pull_values, value_size,
......@@ -512,7 +512,7 @@ int32_t MemorySparseTable::PullSparsePtr(char** pull_values,
auto& local_shard = _local_shards[shard_id];
float data_buffer[value_size];
float* data_buffer_ptr = data_buffer;
for (int i = 0; i < keys.size(); ++i) {
for (size_t i = 0; i < keys.size(); ++i) {
uint64_t key = keys[i].first;
auto itr = local_shard.find(key);
size_t data_size = value_size - mf_value_size;
......@@ -558,7 +558,7 @@ int32_t MemorySparseTable::PushSparse(const uint64_t* keys, const float* values,
size_t update_value_col =
_value_accesor->GetAccessorInfo().update_size / sizeof(float);
for (size_t shard_id = 0; shard_id < _real_local_shard_num; ++shard_id) {
for (int shard_id = 0; shard_id < _real_local_shard_num; ++shard_id) {
tasks[shard_id] = _shards_task_pool[shard_id % _task_pool_size]->enqueue(
[this, shard_id, value_col, mf_value_col, update_value_col, values,
&task_keys]() -> int {
......@@ -566,7 +566,7 @@ int32_t MemorySparseTable::PushSparse(const uint64_t* keys, const float* values,
auto& local_shard = _local_shards[shard_id];
float data_buffer[value_col]; // NOLINT
float* data_buffer_ptr = data_buffer;
for (int i = 0; i < keys.size(); ++i) {
for (size_t i = 0; i < keys.size(); ++i) {
uint64_t key = keys[i].first;
uint64_t push_data_idx = keys[i].second;
const float* update_data =
......@@ -639,7 +639,7 @@ int32_t MemorySparseTable::PushSparse(const uint64_t* keys,
auto& local_shard = _local_shards[shard_id];
float data_buffer[value_col]; // NOLINT
float* data_buffer_ptr = data_buffer;
for (int i = 0; i < keys.size(); ++i) {
for (size_t i = 0; i < keys.size(); ++i) {
uint64_t key = keys[i].first;
uint64_t push_data_idx = keys[i].second;
const float* update_data = values[push_data_idx];
......
paddle/fluid/distributed/ps/table/sparse_accessor.cc
浏览文件 @ c49f35cf
......@@ -171,7 +171,7 @@ bool SparseAccessor::NeedExtendMF(float* value) {
return score >= _config.embedx_threshold();
}
bool SparseAccessor::HasMF(size_t size) {
bool SparseAccessor::HasMF(int size) {
return size > sparse_feature_value.EmbedxG2SumIndex();
}
......@@ -201,7 +201,7 @@ int32_t SparseAccessor::Merge(float** update_values,
for (size_t value_item = 0; value_item < num; ++value_item) {
float* update_value = update_values[value_item];
const float* other_update_value = other_update_values[value_item];
for (auto i = 0u; i < total_dim; ++i) {
for (size_t i = 0; i < total_dim; ++i) {
if (i != SparsePushValue::SlotIndex()) {
update_value[i] += other_update_value[i];
}
......
paddle/fluid/distributed/ps/table/sparse_accessor.h
浏览文件 @ c49f35cf
......@@ -130,7 +130,7 @@ class SparseAccessor : public ValueAccessor {
// 判断该value是否保存到ssd
// virtual bool save_ssd(float* value);
virtual bool NeedExtendMF(float* value);
virtual bool HasMF(size_t size);
virtual bool HasMF(int size);
// 判断该value是否在save阶段dump,
// param作为参数用于标识save阶段，如downpour的xbox与batch_model
// param = 0, save all feature
......
paddle/fluid/distributed/ps/table/sparse_sgd_rule.cc
浏览文件 @ c49f35cf
......@@ -90,7 +90,7 @@ void SparseAdaGradSGDRule::UpdateValueWork(float* w, float* sgd,
float& g2sum = sgd[G2SumIndex()];
double add_g2sum = 0;
for (int i = 0; i < _embedding_dim; i++) {
for (size_t i = 0; i < _embedding_dim; i++) {
double scaled_grad = grad[i] / scale;
w[i] -= learning_rate_ * scaled_grad *
sqrt(_initial_g2sum / (_initial_g2sum + g2sum));
......@@ -103,7 +103,7 @@ void SparseAdaGradSGDRule::UpdateValueWork(float* w, float* sgd,
void SparseAdaGradSGDRule::InitValueWork(float* value, float* sgd,
bool zero_init) {
for (int i = 0; i < _embedding_dim; ++i) {
for (size_t i = 0; i < _embedding_dim; ++i) {
if (zero_init) {
value[i] = 0.0;
BoundValue(value[i]);
......@@ -141,7 +141,7 @@ void StdAdaGradSGDRule::LoadConfig(const SparseCommonSGDRuleParameter& param,
void StdAdaGradSGDRule::UpdateValueWork(float* w, float* sgd, const float* grad,
float scale) {
for (int i = 0; i < _embedding_dim; i++) {
for (size_t i = 0; i < _embedding_dim; i++) {
float& g2sum = sgd[G2SumIndex() + i];
double scaled_grad = grad[i] / scale;
w[i] -= learning_rate_ * scaled_grad *
......@@ -153,7 +153,7 @@ void StdAdaGradSGDRule::UpdateValueWork(float* w, float* sgd, const float* grad,
void StdAdaGradSGDRule::InitValueWork(float* value, float* sgd,
bool zero_init) {
for (int i = 0; i < _embedding_dim; ++i) {
for (size_t i = 0; i < _embedding_dim; ++i) {
if (zero_init) {
value[i] = 0.0;
BoundValue(value[i]);
......@@ -204,7 +204,7 @@ void SparseAdamSGDRule::UpdateValueWork(float* w, float* sgd, const float* grad,
// lr not change in one update
lr *= sqrt(1 - beta2_pow_) / (1 - beta1_pow_);
for (int i = 0; i < _embedding_dim; i++) {
for (size_t i = 0; i < _embedding_dim; i++) {
// Calculation
gsum[i] = _beta1_decay_rate * gsum[i] + (1 - _beta1_decay_rate) * g[i];
g2sum[i] =
......@@ -219,7 +219,7 @@ void SparseAdamSGDRule::UpdateValueWork(float* w, float* sgd, const float* grad,
void SparseAdamSGDRule::InitValueWork(float* value, float* sgd,
bool zero_init) {
for (int i = 0; i < _embedding_dim; ++i) {
for (size_t i = 0; i < _embedding_dim; ++i) {
if (zero_init) {
value[i] = 0.0;
BoundValue(value[i]);
......@@ -233,7 +233,7 @@ void SparseAdamSGDRule::InitValueWork(float* value, float* sgd,
}
}
// init rule gsum and g2sum
for (int i = GSumIndex(); i < Beta1PowIndex(); i++) {
for (size_t i = GSumIndex(); i < Beta1PowIndex(); i++) {
sgd[i] = 0.0;
}
// init beta1_pow and beta2_pow
......
paddle/fluid/distributed/ps/table/ssd_sparse_table.cc
浏览文件 @ c49f35cf
......@@ -58,7 +58,7 @@ int32_t SSDSparseTable::PullSparse(float* pull_values, const uint64_t* keys,
}
std::atomic<uint32_t> missed_keys{0};
for (size_t shard_id = 0; shard_id < _real_local_shard_num; ++shard_id) {
for (int shard_id = 0; shard_id < _real_local_shard_num; ++shard_id) {
tasks[shard_id] =
_shards_task_pool[shard_id % _shards_task_pool.size()]->enqueue(
[this, shard_id, &task_keys, value_size, mf_value_size,
......@@ -67,7 +67,7 @@ int32_t SSDSparseTable::PullSparse(float* pull_values, const uint64_t* keys,
auto& local_shard = _local_shards[shard_id];
float data_buffer[value_size];
float* data_buffer_ptr = data_buffer;
for (int i = 0; i < keys.size(); ++i) {
for (size_t i = 0; i < keys.size(); ++i) {
uint64_t key = keys[i].first;
auto itr = local_shard.find(key);
size_t data_size = value_size - mf_value_size;
......@@ -105,7 +105,8 @@ int32_t SSDSparseTable::PullSparse(float* pull_values, const uint64_t* keys,
memcpy(data_buffer_ptr, itr.value().data(),
data_size * sizeof(float));
}
for (int mf_idx = data_size; mf_idx < value_size; ++mf_idx) {
for (size_t mf_idx = data_size; mf_idx < value_size;
++mf_idx) {
data_buffer[mf_idx] = 0.0;
}
int pull_data_idx = keys[i].second;
......@@ -117,7 +118,7 @@ int32_t SSDSparseTable::PullSparse(float* pull_values, const uint64_t* keys,
return 0;
});
}
for (size_t i = 0; i < _real_local_shard_num; ++i) {
for (int i = 0; i < _real_local_shard_num; ++i) {
tasks[i].wait();
}
if (FLAGS_pserver_print_missed_key_num_every_push) {
......@@ -145,7 +146,7 @@ int32_t SSDSparseTable::PushSparse(const uint64_t* keys, const float* values,
int shard_id = (keys[i] % _sparse_table_shard_num) % _avg_local_shard_num;
task_keys[shard_id].push_back({keys[i], i});
}
for (size_t shard_id = 0; shard_id < _real_local_shard_num; ++shard_id) {
for (int shard_id = 0; shard_id < _real_local_shard_num; ++shard_id) {
tasks[shard_id] =
_shards_task_pool[shard_id % _shards_task_pool.size()]->enqueue(
[this, shard_id, value_col, mf_value_col, update_value_col,
......@@ -154,7 +155,7 @@ int32_t SSDSparseTable::PushSparse(const uint64_t* keys, const float* values,
auto& local_shard = _local_shards[shard_id];
float data_buffer[value_col];
float* data_buffer_ptr = data_buffer;
for (int i = 0; i < keys.size(); ++i) {
for (size_t i = 0; i < keys.size(); ++i) {
uint64_t key = keys[i].first;
uint64_t push_data_idx = keys[i].second;
const float* update_data =
......@@ -196,7 +197,7 @@ int32_t SSDSparseTable::PushSparse(const uint64_t* keys, const float* values,
return 0;
});
}
for (size_t i = 0; i < _real_local_shard_num; ++i) {
for (int i = 0; i < _real_local_shard_num; ++i) {
tasks[i].wait();
}
}
......@@ -228,7 +229,7 @@ int32_t SSDSparseTable::Shrink(const std::string& param) {
int thread_num = _real_local_shard_num < 20 ? _real_local_shard_num : 20;
omp_set_num_threads(thread_num);
#pragma omp parallel for schedule(dynamic)
for (size_t i = 0; i < _real_local_shard_num; ++i) {
for (int i = 0; i < _real_local_shard_num; ++i) {
uint64_t mem_count = 0;
uint64_t ssd_count = 0;
......@@ -264,7 +265,7 @@ int32_t SSDSparseTable::Shrink(const std::string& param) {
int32_t SSDSparseTable::UpdateTable() {
// TODO implement with multi-thread
int count = 0;
for (size_t i = 0; i < _real_local_shard_num; ++i) {
for (int i = 0; i < _real_local_shard_num; ++i) {
auto& shard = _local_shards[i];
// from mem to ssd
for (auto it = shard.begin(); it != shard.end();) {
......@@ -285,7 +286,7 @@ int32_t SSDSparseTable::UpdateTable() {
int64_t SSDSparseTable::LocalSize() {
int64_t local_size = 0;
for (size_t i = 0; i < _real_local_shard_num; ++i) {
for (int i = 0; i < _real_local_shard_num; ++i) {
local_size += _local_shards[i].size();
}
// TODO rocksdb size
......@@ -328,7 +329,7 @@ int32_t SSDSparseTable::Save(const std::string& path,
omp_set_num_threads(thread_num);
#pragma omp parallel for schedule(dynamic)
for (size_t i = 0; i < _real_local_shard_num; ++i) {
for (int i = 0; i < _real_local_shard_num; ++i) {
FsChannelConfig channel_config;
if (_config.compress_in_save() && (save_param == 0 || save_param == 3)) {
channel_config.path = paddle::string::format_string(
......@@ -484,14 +485,14 @@ int64_t SSDSparseTable::CacheShuffle(
int feasign_size = 0;
std::vector<paddle::framework::Channel<std::pair<uint64_t, std::string>>>
tmp_channels;
for (size_t i = 0; i < _real_local_shard_num; ++i) {
for (int i = 0; i < _real_local_shard_num; ++i) {
tmp_channels.push_back(
paddle::framework::MakeChannel<std::pair<uint64_t, std::string>>());
}
omp_set_num_threads(thread_num);
#pragma omp parallel for schedule(dynamic)
for (size_t i = 0; i < _real_local_shard_num; ++i) {
for (int i = 0; i < _real_local_shard_num; ++i) {
paddle::framework::ChannelWriter<std::pair<uint64_t, std::string>>& writer =
writers[i];
// std::shared_ptr<paddle::framework::ChannelObject<std::pair<uint64_t,
......@@ -520,7 +521,7 @@ int64_t SSDSparseTable::CacheShuffle(
<< " and start sparse cache data shuffle real local shard num: "
<< _real_local_shard_num;
std::vector<std::pair<uint64_t, std::string>> local_datas;
for (size_t idx_shard = 0; idx_shard < _real_local_shard_num; ++idx_shard) {
for (int idx_shard = 0; idx_shard < _real_local_shard_num; ++idx_shard) {
paddle::framework::ChannelWriter<std::pair<uint64_t, std::string>>& writer =
writers[idx_shard];
auto channel = writer.channel();
......@@ -543,8 +544,8 @@ int64_t SSDSparseTable::CacheShuffle(
send_index[i] = i;
}
std::random_shuffle(send_index.begin(), send_index.end());
for (auto index = 0u; index < shuffle_node_num; ++index) {
int i = send_index[index];
for (int index = 0; index < shuffle_node_num; ++index) {
size_t i = send_index[index];
if (i == _shard_idx) {
continue;
}
......@@ -624,7 +625,7 @@ int32_t SSDSparseTable::Load(const std::string& path,
}
//加载path目录下数据[start_idx, end_idx)
int32_t SSDSparseTable::Load(size_t start_idx, size_t end_idx,
int32_t SSDSparseTable::Load(size_t start_idx, int end_idx,
const std::vector<std::string>& file_list,
const std::string& param) {
if (start_idx >= file_list.size()) {
......@@ -688,7 +689,7 @@ int32_t SSDSparseTable::Load(size_t start_idx, size_t end_idx,
continue;
}
}
int value_size =
size_t value_size =
_value_accesor->ParseFromString(++end, data_buffer_ptr);
// ssd or mem
if (_value_accesor->SaveSSD(data_buffer_ptr)) {
......
paddle/fluid/distributed/ps/table/ssd_sparse_table.h
浏览文件 @ c49f35cf
......@@ -55,7 +55,7 @@ class SSDSparseTable : public MemorySparseTable {
int32_t Flush() override { return 0; }
virtual int32_t Shrink(const std::string& param) override;
virtual void Clear() override {
for (size_t i = 0; i < _real_local_shard_num; ++i) {
for (int i = 0; i < _real_local_shard_num; ++i) {
_local_shards[i].clear();
}
}
......@@ -79,7 +79,7 @@ class SSDSparseTable : public MemorySparseTable {
virtual int32_t Load(const std::string& path,
const std::string& param) override;
//加载path目录下数据[start_idx, end_idx)
virtual int32_t Load(size_t start_idx, size_t end_idx,
virtual int32_t Load(size_t start_idx, int end_idx,
const std::vector<std::string>& file_list,
const std::string& param);
int64_t LocalSize();
......
paddle/fluid/distributed/ps/wrapper/fleet.cc
浏览文件 @ c49f35cf
......@@ -536,8 +536,8 @@ void FleetWrapper::PushSparseFromTensorAsync(
output_len = 0;
if (tensor->lod().size() > 0) {
for (size_t i = 0; i < tensor->lod()[0].size() - 1; ++i) {
for (int j = tensor->lod()[0][i]; j < tensor->lod()[0][i + 1];
for (int i = 0; i < tensor->lod()[0].size() - 1; ++i) {
for (size_t j = tensor->lod()[0][i]; j < tensor->lod()[0][i + 1];
++j, output_len += fea_dim) {
uint64_t real_id = static_cast<uint64_t>(ids[j]);
if (real_id == padding_id) {
......@@ -566,7 +566,7 @@ void FleetWrapper::PushSparseFromTensorAsync(
}
}
} else {
for (size_t i = 0; i < len; ++i, output_len += fea_dim) {
for (int i = 0; i < len; ++i, output_len += fea_dim) {
uint64_t real_id = static_cast<uint64_t>(ids[i]);
if (real_id == padding_id) {
continue;
......
paddle/fluid/distributed/test/brpc_service_dense_sgd_test.cc
浏览文件 @ c49f35cf
......@@ -222,7 +222,7 @@ void RunBrpcPushDense() {
worker_ptr_->PullDense(temp_region.data(), temp_region.size(), 0);
pull_status.wait();
for (size_t idx = 0; idx < tensor->numel(); ++idx) {
for (int64_t idx = 0; idx < tensor->numel(); ++idx) {
EXPECT_FLOAT_EQ(temp[idx], 1.0);
}
......@@ -236,7 +236,7 @@ void RunBrpcPushDense() {
pull_status = worker_ptr_->PullDense(regions.data(), regions.size(), 0);
pull_status.wait();
for (size_t idx = 0; idx < tensor->numel(); ++idx) {
for (int64_t idx = 0; idx < tensor->numel(); ++idx) {
EXPECT_FLOAT_EQ(w[idx], float(idx));
}
......@@ -265,7 +265,7 @@ void RunBrpcPushDense() {
worker_ptr_->PullDense(regions.data(), regions.size(), 0);
pull_update_status.wait();
for (size_t idx = 0; idx < tensor->numel(); ++idx) {
for (int64_t idx = 0; idx < tensor->numel(); ++idx) {
EXPECT_FLOAT_EQ(w[idx], float(idx) - 1.0);
}
......
paddle/fluid/distributed/test/sparse_sgd_rule_test.cc
浏览文件 @ c49f35cf
......@@ -89,25 +89,25 @@ TEST(downpour_sparse_adagrad_test, test_init_and_update) {
rule.InitValue(w, w + 10, true);
for (auto i = 0u; i < kEmbSize; ++i) {
for (int i = 0; i < kEmbSize; ++i) {
ASSERT_FLOAT_EQ(w[i], 0);
}
ASSERT_FLOAT_EQ(w[kEmbSize], 0);
// check init_value for random
rule.InitValue(w, w + 10, false);
for (auto i = 0u; i < kEmbSize; ++i) {
for (int i = 0; i < kEmbSize; ++i) {
ASSERT_TRUE(w[i] >= rule.MinBound() && w[i] <= rule.MaxBound());
}
ASSERT_FLOAT_EQ(w[kEmbSize], 0);
// check update_value for one field
for (auto i = 0u; i < kEmbSize; ++i) {
for (int i = 0; i < kEmbSize; ++i) {
w[i] = 0;
}
w[kEmbSize] = 0;
float grad[kEmbSize];
for (auto i = 0u; i < kEmbSize; ++i) {
for (int i = 0; i < kEmbSize; ++i) {
grad[i] = (i + 1) * 1.0;
}
......@@ -185,7 +185,7 @@ TEST(downpour_sparse_adam_test, test_init_and_update) {
rule.UpdateValue(value, value + embed_dim, grad);
for (auto i = 0u; i < value_dim; ++i) { // check update
for (int i = 0; i < value_dim; ++i) { // check update
ASSERT_FLOAT_EQ(value[i], label[i]) << "i is " << i;
}
}
......
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